I am a post-doctoral fellow in the Computer Science Department at Carnegie Mellon University, working with Prof. Zico Kolter. My research focuses on developing robust learning algorithms for various machine learning areas. These include classification, regression, fairness in ML, graphical models, structured prediction, and differentiable programming. I am also interested in broader areas of machine learning theory and applications.
I received my Ph.D. from the Department of Computer Science, University of Illinois at Chicago. I was fortunate to have Prof. Brian Ziebart as my advisor. I was grateful to also collaborate with Prof. Xinhua Zhang. My thesis proposed distributionally-robust algorithms that combine the benefit of the large-margin models in terms of their flexibility with the probabilistic models in terms of their statistical properties.
I was born and raised in a small village in Java island, Indonesia. After high school, I moved to Jakarta to complete my bachelor degree at the Institute of Statistics. I was also very lucky to be awarded Fulbright scholarship in 2012. Being a Fulbright grantee was a milestone to pursue my passion in machine learning research.
I am fond of nature. In my spare time, I enjoy hiking in national parks and local forests with my family.
Selected Publications: [All Publications]
Performance-Aligned Learning Algorithms with Statistical Guarantees PhD Thesis: University of Illinois at Chicago 2019
Fair Logistic Regression: An Adversarial Perspective Preprint: arXiv preprint 2019
Distributionally Robust Graphical Models Conference: Advances in Neural Information Processing Systems (NeurIPS) 2018
Consistent Robust Adversarial Prediction for General Multiclass Classification Preprint: arXiv preprint 2018
Adversarial Multiclass Classification: A Risk Minimization Perspective Conference: Advances in Neural Information Processing Systems (NeurIPS) 2016